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MoKa

Accurate pKa prediction and automatic structure modification is critical
for many computational chemistry methods which are strongly dependent on
the tautomerization and protonation state of the structures, including
docking, binding affinity estimation, QSAR and ADME modelling, and
metabolism prediction.

MoKa implements a novel approach [1] for in-silico computation of pKa
values; trained using a very diverse set of more than 25000 pKa values, it
provides accurate and fast calculations using an algorithm based on
descriptors derived from GRID molecular interaction fields.